RAG Engineering: Building AI That Knows Your Data

Large language models are remarkably capable, but they share a common limitation: they only know what was in their training data. They cannot access your company's internal documents, your latest product specs, or yesterday's support tickets. Retrieval-Augmented Generation (RAG) solves this by connecting LLMs to external knowledge sources at inference time, letting them answer questions grounded in your actual data. This course teaches you to design, build, and deploy production-grade RAG systems from the ground up.

Module I — Foundations (Lessons 1-3): Understand why RAG matters, how LLM limitations create the need for external knowledge retrieval, and the core technologies that make it possible. You will learn how embeddings encode meaning into vectors, explore the landscape of vector databases, and understand the tradeoffs between managed services and self-hosted solutions.

Module II — Data Pipeline (Lessons 4-6): Master the art of preparing data for retrieval. You will process documents from diverse formats, implement chunking strategies that balance precision and context, and apply advanced retrieval techniques including hybrid search, re-ranking, and metadata filtering to get the right information to the model.

Module III — Building and Advancing (Lessons 7-9): Assemble complete RAG pipelines using LangChain and LlamaIndex, then push beyond basic retrieve-and-generate with advanced patterns like multi-query RAG, HyDE, agentic RAG, and code-specific retrieval systems that understand programming languages at a structural level.

Module IV — Production and Practice (Lessons 10-12): Evaluate your RAG system with rigorous metrics using RAGAS and LLM-as-judge techniques, then harden it for production with caching, monitoring, security, and cost optimization. The course culminates in a hands-on capstone project where you build a complete knowledge base system end to end.

Whether you are an ML engineer adding retrieval to your LLM applications, a backend developer building AI-powered search, or a technical founder prototyping a knowledge product, this course gives you the practical skills to ship RAG systems that work reliably in the real world.